Secure multi-party computation (SMC) allows parties to jointly compute a function over their inputs, while keeping every input confidential. It has been extensively applied in privacy-preserving computation, such as privacy-preserving data mining (PPDM), to protect data privacy. However, most SMC-based solutions are ad-hoc. They are proposed for specific applications, and thus cannot be applied to other applications directly. To address this issue, we propose a privacy model DAG (Directed A cyclic Graph) that consists of a set of secure operators (e.g., Multiplication and division). Our DAG model is general -- its operators, if pipelined together, can implement various functions. It is also extendable -- secure operators can be defined to add new features to the model. As an application study, we have applied our DAG to kernel regression. Experiments on datasets of more than 680,000 tuples show that our DAG model is effective and its running time is nearly thrice that of non-privacy setting, where parties directly disclose data.